构建数字孪生框架，系统验证越野自动驾驶车辆在复杂环境下的性能。
A Systematic Digital Engineering Approach to Verification & Validation of Autonomous Ground Vehicles in Off-Road Environments
- 融合数字孪生与MBSE/MBD，实现需求可追溯、测试自动化。
- 生成128组测试用例，覆盖不同时间、天气与算法组合。
- 适合自动驾驶系统开发与验证团队使用。
当前工程界在越野地面车辆自主算法的系统性开发与验证方面面临重大挑战，主要源于极高的测试参数和算法变体。为此，本文提出一种优化的数字工程框架，将数字孪生仿真与基于模型的系统工程（MBSE）及基于模型的设计（MBD）流程紧密集成。通过一个端到端案例研究，验证了自主轻型战术车辆（LTV）在泥泞道路执行视觉伺服导航并响应障碍物或环境变化的能力。该方法支持可追溯的需求工程、高效的变体管理、细粒度参数扫描设置、系统化测试用例定义以及模拟的自动化执行。候选越野自主算法在128个测试用例下被评估是否满足要求，这些用例根据测试参数（时段与天气条件）及算法变体（感知、规划、控制子系统）自动生成。最终测试结果与关键性能指标被记录，测试报告自动输出，从而实现贯穿数字主线的手动与自动化数据分析，具备良好的可追溯性与可追踪性。
原文摘要 · Abstract (English)
The engineering community currently encounters significant challenges in the systematic development and validation of autonomy algorithms for off-road ground vehicles. These challenges are posed by unusually high test parameters and algorithmic variants. In order to address these pain points, this work presents an optimized digital engineering framework that tightly couples digital twin simulations with model-based systems engineering (MBSE) and model-based design (MBD) workflows. The efficacy of the proposed framework is demonstrated through an end-to-end case study of an autonomous light tactical vehicle (LTV) performing visual servoing to drive along a dirt road and reacting to any obstacles or environmental changes. The presented methodology allows for traceable requirements engineering, efficient variant management, granular parameter sweep setup, systematic test-case definition, and automated execution of the simulations. The candidate off-road autonomy algorithm is evaluated for satisfying requirements against a battery of 128 test cases, which is procedurally generated based on the test parameters (times of the day and weather conditions) and algorithmic variants (perception, planning, and control sub-systems). Finally, the test results and key performance indicators are logged, and the test report is generated automatically. This then allows for manual as well as automated data analysis with traceability and tractability across the digital thread.
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